Bibliographic record
Abstract
The States and Traits of EEG VariabilityErin Gibson Doctor of Philosophy Institute of Medical Science University of Toronto 2021 Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in noisy, highly variable patterns of ongoing activity. Yet the functional significance and behavioral consequences of this variability remains largely unknown. We hypothesize that brain signal variability is tightly coupled to the processing of task-relevant information and serves as an important indicator of cognitive function. To test this, we examine EEG activity in young, healthy adults as they perform a cognitive skill learning and resting state task. Several measures of EEG variability and signal strength are calculated at multiple timescales, or in overlapping time windows that span the trial interval. We perform a systematic examination of the factors that most strongly influence the variability and strength of EEG activity. Study 1 examines the relative sensitivity of each measure to trait-level variation across subjects and state-level variation across task blocks. We find that EEG variability is most sensitive to trait-level differences across individuals that remain relatively stable across blocks. Study 2 examines the sensitivity of each measure to different sources of state-level variation across task blocks. We find that key task-driven changes in EEG activity are better reflected in the strength, rather than the variability, of EEG activity. Study 3 examines trait-level variation in each measure and its relationship with behavior. We find that differences in learning speed and acquired skill across individuals are better reflected in relative changes in the variability, rather than the strength, of EEG activity. These results demonstrate that EEG variability is particularly sensitive to stable trait-level variation across individuals and relative changes in EEG variability around a particular level, rather than the level itself, are a strong indicator of behavioral performance in young, healthy adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".